code: def test_bbox_head_refine_bboxes(self):
      num_classes = 6
      bbox_head = BBoxHead(
          reg_class_agnostic=True, num_classes=num_classes
      )
      s = 128
      img_metas = [
          {
              "img_shape": (s, s, 3),
              "scale_factor": 1,
          }
      ]
      sampling_results = [SamplingResult.random()]
      num_samples = 20
      rois = torch.rand((num_samples, 4))
      roi_img_ids = torch.zeros(num_samples, 1)
      rois = torch.cat((roi_img_ids, rois), dim=1)
      cls_scores = torch.rand((num_samples, num_classes + 1))
      bbox_preds = torch.rand((num_samples, 4))
      labels = torch.randint(0, num_classes + 1, (num_samples,)).long()
      bbox_targets = (labels, None, None, None)
      bbox_results = dict(
          rois=rois,
          bbox_pred=bbox_preds,
          cls_score=cls_scores,
          bbox_targets=bbox_targets,
      )
  
      bbox_list = bbox_head.refine_bboxes(
          sampling_results=sampling_results,
          bbox_results=bbox_results,
          batch_img_metas=img_metas,
      )
  
      self.assertGreaterEqual(num_samples, len(bbox_list[0]))
      self.assertIsInstance(bbox_list[0], InstanceData)
      self.assertEqual(bbox_list[0].bboxes.shape[1], 4)
  
file_path: mmdetection/tests/test_models/test_roi_heads/test_bbox_heads/test_bbox_head.py
imports: from unittest import TestCase
  import torch
  from mmengine.config import ConfigDict
  from mmengine.structures import InstanceData
  from mmdet.models.roi_heads.bbox_heads import BBoxHead, Shared2FCBBoxHead, Shared4Conv1FCBBoxHead
  from mmdet.models.task_modules.samplers import SamplingResult
label_desc: Use image node_id 3 for calling the TestBboxHead obj's underlying member method code with example usage: obj.test_bbox_head_refine_bboxes() without return types